Layer One

Most ecommerce search experiences still depend on rigid filters, keyword matching, and static navigation. But buyers, especially in complex B2B environments, increasingly expect systems to understand intent, context, and ambiguity naturally.

In our latest article, we explore how SitecoreAI can be used to build agentic product discovery experiences that go far beyond traditional search. By combining AI orchestration, retrieval pipelines, structured product data, and contextual reasoning, commerce systems can guide users to the right products rather than forcing them to manually navigate massive catalogs.

The article also digs into the operational side of AI-powered commerce: adaptive retrieval, clarification workflows, observability, ranking governance, and the importance of connecting AI to real product and customer data. The goal is not just “adding AI to search.” It is creating systems that can continuously adapt and improve product discovery over time.

A lot of the conversation around AI in commerce focuses on demos. We wanted to focus on what implementation actually looks like in practice.

Read the full article here: https://www.layerone.us/library/building-agentic-product-discovery-with-sitecoreai/

Building Agentic Product Discovery with SitecoreAI SitecoreAI is far more interesting as an orchestration layer than a content generation feature. This article explores how adaptive product discovery can coordinate search, customer context, structured product data, and retrieval confidence in real time using Sitecore Search, CDP, XM Cloud, and AI-dr...

Wire and cable pricing is getting harder to trust.

Copper volatility is the obvious reason, but it is not the only thing teams are dealing with. A lot of pricing workflows were built around slower-moving inputs, periodic updates, and human interpretation. That creates a gap between what has changed in the market and what the quote actually reflects.

In a market like this, that gap gets expensive fast.

A quote can be off because copper moved. It can also be off because the product structure does not clearly reflect the real cost drivers: gauge, conductor count, conductor material, approvals, put-ups, minimums, lead times, substitutions, and availability.

That is where margin can quietly disappear.

The teams adapting fastest are connecting pricing, product data, availability, and quoting logic into one coordinated workflow, so cost inputs, product attributes, and customer-facing quote outputs move together.

That is what makes quoting easier to trust.

We wrote more about why wire and cable pricing models are breaking in 2026, and what a more responsive model looks like.

https://layerone.us/library/why-wire-cable-pricing-models-are-breaking-in-2026/

Why Wire & Cable Pricing Models Are Breaking in 2026 Wire and cable pricing models were built for stable markets. That assumption is breaking. Copper volatility, electrification demand, and supply pressure are exposing a deeper issue: pricing systems that cannot keep up with reality. The result is not just margin pressure. It is operational friction a...